Stripe is hiring a ML Engineer Manager in Toronto!
Stripe is hiring an ML engineering manager to lead its newly formed Conversation Platform team in Toronto. The group is building a merchant-facing conversation platform that automates straightforward support and product tasks while helping users work through more complex ones. Example applications include personalized integration suggestions, natural-language control of Stripe’s API, and automated issue resolution. The team works end to end, moving from early ideas and prototypes through model development and production deployment, using retrieval-augmented generation with current LLMs as well as fine-tuned models. The manager will set both technical and process direction, shape an ambitious AI/ML vision, coordinate product integrations with partner teams, and stay close to hands-on experimentation. This is also a people-leadership role: hiring, developing, and coaching a high-performing ML team while contributing to Stripe’s broader internal and external ML community. The ideal candidate has prior experience as an ML engineer or applied scientist, has shipped ML systems, and can lead through ambiguity in a fast-moving, cross-functional environment.
Key responsibilities
- Set an ambitious AI/ML vision that improves merchant and user outcomes
- Define the team’s technical direction and operating processes in line with business priorities
- Coordinate product integrations and roadmaps with partner teams
- Explore new ideas and build prototypes that can become production systems
- Hire, develop, and coach a strong ML engineering team
- Maintain a high bar for production quality and help engineers grow in their careers
- Participate in and contribute to the wider internal and external ML community
Key requirements
- At least four years managing ML teams
- Prior experience as a machine learning engineer, applied scientist, or similar individual contributor
- Experience building and shipping ML systems to production
- Comfort leading in ambiguous, high-growth, high-impact environments
- Strong collaboration skills in cross-functional settings
- A high standard for production systems and technical quality
- Seniority: Manager
- Work mode: Not specified
- Key skills: Machine learning, LLM, RAG, Streaming feature pipelines, Production ML systems, Team management, Hiring and coaching, Cross-functional collaboration, Prototyping
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